Deception detection has been receiving an increasing amount of attention from the computational linguistics, speech, and multimodal processing communities. One of the major challenges encountered in this task is the availability of data, and most of the research work to date has been conducted on acted or artificially collected data. The generated deception models are thus lacking real-world evidence. In this paper, we explore the use of multi-modal real-life data for the task of deception detection. We develop a new deception dataset consisting of videos from real-life scenarios, and build deception tools relying on verbal and nonverbal features. We achieve classification accuracies in the range of 77-82% when using a model that extracts and fuses features from the linguistic and visual modalities. We show that these results outperform the human capability of identifying deceit.
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